User Modelling for Personalised Dressing Assistance by Humanoid Robots
File(s)
Accepted version
Author(s)
Gao, Y
Chang, HJ
Demiris, Y
Type
Conference Paper
Abstract
Assistive robots can improve the well-being of disabled or frail human users by reducing the burden that activities of daily living impose on them. To enable personalised assistance, such robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. In this paper, we present an end-to-end approach for home-environment assistive humanoid robots to provide personalised assistance through a dressing application for users who have upper-body movement limitations. We use randomised decision forests to estimate the upper-body pose of users captured by a top-view depth camera, and model the movement space of upper-body joints using Gaussian mixture models. The movement space of each upper-body joint consists of regions with different reaching capabilities. We propose a method which is based on real-time upper-body pose and user models to plan robot motions for assistive dressing. We validate each part of our approach and test the whole system, allowing a Baxter humanoid robot to assist human to wear a sleeveless jacket.
Date Issued
2015-10-02
Date Acceptance
2015-06-29
Citation
2015, pp.1840-1845
Publisher
IEEE
Start Page
1840
End Page
1845
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
International Conference on Intelligent Systems and Robots (IROS)
Publication Status
Published
Start Date
2015-09-28
Finish Date
2015-10-02
Coverage Spatial
Hamburg, Germany